Abstract

Recent advances in automatic speech recognition (ASR) technology
continue to be based heavily on data-driven methods,
meaning that the full benefits of such research are often not enjoyed
in domains for which there is little training data. Moreover,
tractability is often an issue with these methods when conditioning
for long-distance dependencies, entailing that many
higher-level knowledge sources such as situational knowledge
cannot be easily utilized in classification. This paper describes
an effort to circumvent this problem by using dynamic contextual
knowledge to rescore ASR lattice output using a dynamic
weighted constraint satisfaction function. With this method, it
was possible to achieve a roughly 80% reduction in WER for
ASR in the context of an air traffic control scenario.

Document Type:

Conference or Workshop Item (Paper)

Title:

Knowledge-BasedWord Lattice Rescoring in a Dynamic Context

Authors:

Authors

Institution or Email of Authors

Shore, Todd

Uni Saarland

Faubel, Friedrich

Uni Saarland

Helmke, Hartmut

Hartmut.Helmke@dlr.de

Klakow, Dietrich

Uni Saarland

Date:

September 2012

Refereed publication:

Yes

In ISI Web of Science:

No

Status:

Published

Keywords:

lattice rescoring, knowledge-based, contextsensitivity

Event Title:

InterSpeech 2012

Event Location:

Portland, Oregon USA

Event Type:

international Conference

Event Dates:

09.09.-13.09.2012

Organizer:

Center for Spoken Language Understanding, under the sponsorship of the International Speech Communication Association (ISCA)